UC Santa Barbara

This study develops an integrative methodological framework that combines forensic hydrology, artificial neural networks (ANNs), and physically-based hydrological modeling to assess the cascading impacts of projected land use/land cover (LULC) changes on future flood dynamics in the upstream watershed of Golestan Dam, Iran. A large-magnitude flood event analogous to the destructive 2019 flood was simulated under evolving LULC scenarios for three future horizons: 2030, 2040, and 2050. The curve number (CN) and lag time (Tlag) were reconstructed using linear and nonlinear interpolation and projected with ANN-based regression modeling. Mann-Kendall trend analysis was applied to assess the temporal consistency of ANN-predicted CN and Tlag series and support the selection of the most reliable synthetic datasets. The simulations revealed highly non-linear growth in peak discharges, with the Qarah Shur sub-basin projected to experience a 163.78% increase in flood intensity by 2050, resulting from a substantial increase in the contribution of barren land to flood intensification (from 22.89% to 78.88%) and a threefold increase in the contribution of sparse vegetation (from 9.04% to 31.13%). These two factors, individually and in combination, were identified as the most critical contributors to flood amplification in this sub-basin. The Galikesh sub-basin followed with a 68.99% increase by 2050, being the only sub-basin where the contribution of road expansion (from 8.88% to 27.41%) emerged as the dominant factor driving flood intensification. The Tamar Gorgan and Tangerah sub-basins demonstrated more moderate increases in discharge, 13.89% and 10.44%, respectively, driven by modest land degradation and road building. This work’s findings underscore growing risks to infrastructure, including embankment instability, bridge scour, and urban inundation, and to groundwater recharge, aquatic ecosystems, and water quality through the mobilization of pollutants and sediments under increasing flood risk. The spatial comparison of sub-basin-level responses captures the heterogeneity of flood vulnerability and the urgency of instituting adaptive land use policies grounded in hydrological sensitivity, riparian corridor protection, and nature-based flood mitigation.

In the context of a knowledge economy and innovation-driven development, innovation districts have emerged as place-based urban development tools increasingly embedded within the urban redevelopment of old districts. While this economically and industrially oriented redevelopment pattern revitalizes urban spaces, it also raises concerns about social integration and community inclusion. This study proposes using cognitive regions as a key lens to assess whether urban redevelopment trends toward social integration or segregation, examining the cognitive coupling that occurs when innovation-driven selective redevelopment is inserted into the context of Chinese urban villages. Using Tangdong Village in Guangzhou as a case study, we apply an enhanced cognitive interview (ECI) method combined with visual and spatial analyses to identify cognitive disconnections between different groups toward original residential and innovation spaces, manifested in differences in cognitive elements, focal points, core ranges, and meaning interpretations. At the same time, the transitional streets and environmental improvements surrounding the innovation spaces are weakening the perceived boundaries between the two. We further outline the potential phased evolution of the cognitive impacts of this pattern and highlight that balancing the needs of different groups throughout this process is key to achieving inclusive and sustainable redevelopment.

Inferring mechanical interactions from the spatial organization of discrete damage events is a fundamental inverse problem in the mechanics of heterogeneous materials. This work develops a statistical framework for extracting load-transfer interactions from three-dimensional fiber fracture data in unidirectional fiber-reinforced composites. Conventional micromechanical models typically assume global load sharing (GLS), implying statistically independent fiber fracture events, whereas growing experimental evidence suggests that load redistribution can become localized, producing spatially autocorrelated fracture. To distinguish these effects, classical second-order spatial statistics are extended by decomposing pairwise separations into transverse and axial components, reflecting the directional asymmetry of stress redistribution in fiber composites. Analytical solutions are derived for two canonical GLS models that establish a mechanics-based null hypothesis for mechanical independence. Unlike conventional approaches based on complete spatial randomness, this null hypothesis explicitly accounts for both the underlying fiber arrangement and the observed axial break intensity profile, allowing arbitrary fracture distributions to be analyzed without assuming a particular axial form. A normalized fiber interaction statistic is then introduced to remove these contributions, enabling direct assessment of mechanically induced coupling between neighboring fibers and meaningful comparison among datasets with different fiber arrangements and break distributions. Monte Carlo simulations quantify estimator variability and demonstrate that localized interactions can be detected with high statistical power even when only a modest fraction of fiber breaks are mechanically coupled. The resulting framework provides a rigorous methodology for interpreting volumetric fracture data and establishes a quantitative bridge between experimentally observed spatial damage patterns and micromechanical load-sharing models. Although demonstrated for fiber fracture in unidirectional composites, the underlying methodology is broadly applicable to heterogeneous materials in which damage evolves through discrete spatial events.

Recognition of large f-block ions underlies advances in targeted radionuclide therapy and rare-earth element separations. Like the lanthanide-binding protein lanmodulin, the 18-membered macrocycle macropa displays reverse-size selectivity characterized by its preference for binding large metal ions. Its diaza-18-crown-6 scaffold enables efficient complexation of therapeutically relevant radiometals, such as ²²⁵Ac, ²²³Ra, and ²¹³Bi, while accommodating diagnostic partners, including ²⁰³Pb and radiolanthanides. In parallel, systematic differences in stability constants across the lanthanide series enable size-based discrimination in separation chemistry. Macropa and its derivatives have been deployed in different strategies to recover and purify rare-earth elements and minor actinides. This manuscript describes recent studies on modifications of macropa, including cavity expansion, alteration of donor atoms, backbone rigidification, chelator-embedded ¹⁸F incorporation, and acyclic variants to demonstrate that effective selectivity arises from balancing preorganization, donor strength, and conformational adaptability. These studies establish macropa-based scaffolds as synthetic systems that bridge radiopharmaceutical coordination chemistry and rare-earth element separations.

Background: Strengths-based narratives of autism often presume that autistic individuals’ expertise will naturally translate into better outcomes for the teams they work with. However, such perspectives overlook the fundamentally relational nature of collaborative work, in which teams infer, evaluate, and recognize members’ knowledge and abilities through social interaction. When autistic communication and cognitive styles diverge from neurotypical teamwork norms, autistic individuals’ expertise may remain unrecognized in team contexts. Methods: Our data consist of semi-structured interviews with 35 autistic adults from a larger study and two newly collected serial focus groups with five additional autistic adults, all of whom had experience with team-based collaboration in academic or workplace settings. Using phronetic iterative qualitative data analysis, we identified recurring patterns in participants’ lived experiences of teamwork. Results: We identified three predominant structural barriers that limit autistic people’s access to and participation in teamwork: (a) Autonomy , reflecting a preference for independent work often arising from mismatches with dominant collaborative norms; (b) anxiety , shaped by lifelong experiences of stigma and neuronormative social expectations that constrain knowledge sharing; and (c) attention , referring to the cognitive, social, and sensory demands of teamwork that limit sustained engagement. Together, these barriers can operate incrementally, filtering out autistic individuals’ contributions before teams integrate them into their collective knowledge. Conclusion: These findings reframe autistic individuals’ challenges in neurodiverse teamwork as structural rather than individual. By highlighting how neuronormative team environments constrain the visibility of autistic expertise, this study contributes to autism and organizational research as well as team cognition theory. The findings also suggest the need for structural changes in team environments that normalize diverse communication styles, work preferences, and approaches to collaboration to foster more neuroinclusive teamwork. Community Brief Why is this an important issue? Many organizations and universities say they value the strengths autistic individuals bring. But autistic individuals still face barriers when working in teams—barriers that limit how much they can participate and whether their contributions get noticed. What was the purpose of this study? We wanted to understand teamwork from autistic individuals’ lived experiences, focusing on what makes it difficult for them to fully contribute their knowledge and have it recognized by teammates. What did the researchers do? We analyzed interviews from a larger study involving 35 autistic adults with teamwork experience and combined these insights with newly collected focus group data from five autistic adults. Across two focus group sessions held 2 weeks apart, participants completed hands-on activities to reflect on positive and challenging experiences working in teams. What were the results and conclusions of the study? We found three barriers that can make it harder for autistic people to participate fully and have their contributions noticed in teams. First, many participants wanted autonomy , or more control over how and when they completed their work, which would create an initial hurdle to engaging in teamwork. Second, anxiety around change (e.g., new members) and being misunderstood often led participants to hold back ideas or participate less. Third, fast-moving team environments that demand constant attention can be exhausting and make it hard to stay engaged over time. We argue that these are not simply personal challenges stemming from autistic traits. Rather, common expectations about how teams should work (e.g., constant availability, thinking out loud, making small talk, adapting quickly to change) can make participation more difficult for autistic people. What is new or controversial about these findings? This study shows that challenges autistic individuals face in teams are not just personal but they are built into how teams work. When every team member is expected to think and communicate the same way, autistic people can be left out, even when they have the knowledge, skills, and abilities the team needs. What are potential weaknesses in the study? This study used a small sample, and most participants had lower support needs. It also focused on teamwork involving sharing ideas, solving problems, and completing academic or professional projects. The findings may not apply to all autistic people or all types of teams. How will these findings help autistic adults now or in the future? These findings suggest that supporting autistic people requires changing how teams are designed and work, rather than asking autistic individuals to adapt or improve their social skills. Organizations and universities can create room for different ways of communicating and working. For example, teams can let people contribute at different times, protect time for uninterrupted work, and switch between short team check-ins and independent work. Recognizing autistic strengths is an important first step. Meaningful inclusion also requires team environments where autistic people can contribute fully and be recognized for what they bring.

We revisit the problem of correcting the trigonometric moments and the resultant lengths that are obtained from grouped circular data. In circular measurements as elsewhere, it is common in many practical applications to aggregate the data into intervals either for convenience, or because of lack of accuracy in measuring, and then assuming that all the observations in a given interval lie at the midpoint of that interval, for calculating needed statistics. It then becomes important to ask for formulae necessary to adjust for the error introduced by such approximations, and this problem has been considered earlier in Greenwood (1959) and Mardia (1972) focusing on the first two trigonometric moments, because they are the ones used mostly in practice. The proofs provided there also rely on other outside results. We take it considerably further with our principal contributions being twofold: (i) unlike earlier derivations, we provide very simple and straightforward derivations which are complete and hold for trigonometric moments of any order and (ii) make recommendations, based on extensive simulations, as to when and where such corrections make a significant difference. To further support our mathematical derivations, we provide extensive simulations using data that may come from 3 potentially common models, namely, the von Mises, the Generalized von Mises, and the Sine-skewed von Mises, and assess the performance of the correction formulae in these contexts. Our results demonstrate that the corrected trigonometric moments yield more accurate estimates, especially when the underlying distribution that generated the data deviate considerably from uniformity. The methods are illustrated using cross-bedding azimuth data from the Kamthi Formation, showing that grouping resolution can materially affect inference on directional concentration.

Abstract: Previously, the dinuclear persulfide-bridged ruthenium complex [(H2O)(NH3)4Ru(μ-S2)Ru(NH3)4(OH2)]4+ was demonstrated to be a reduction-activated H2S donor, acting as a promising complex for the biological delivery of this gasotransmitter. Building on previous studies, other complexes bearing the [RuSSRu] motif were investigated for H2S release. In this study, a previously reported persulfide-bridged complex [(acac)(Me3TACN)Ru(μ-S2)Ru(Me3TACN)(acac)]2+ was evaluated. The crystal structure of a new crystal form of this complex was determined revealing a new orientation of supporting ligands about the trans-[RuSSRu] motif. Furthermore, resonance Raman spectroscopy revealed symmetric Ru–S and S–S stretching frequencies of 418 and 528 cm–1, respectively. Lastly, the electrochemistry of this complex was probed in aqueous buffer revealing an irreversible reduction at −758 mV vs SCE. The reactivity of [(acac)(Me3TACN)Ru(μ-S2)Ru(Me3TACN)(acac)]2+ was studied in the presence of different biological reductants and unexpectedly remained intact, showing no evidence for S–S bond cleavage or release of H2S. To investigate the difference between this complex and [(H2O)(NH3)4Ru(μ-S2)Ru(NH3)4(OH2)]4+, density functional theory (DFT) calculations were performed, which revealed that [(acac)(Me3TACN)Ru(μ-S2)Ru(Me3TACN)(acac)]2+ possesses unoccupied ligand-based π* orbitals that more readily accept electrons than S–S bond destabilizing σ* orbitals. These computational findings validate our experimental results and provide guiding principles required for future compound design.

Abnormal deposition of the microtubule-associated protein tau has long been associated with spine loss and neuronal death in neurodegenerative diseases. Elucidating how pathological tau affects synaptic activity in vivo and whether individual synaptic properties dictate the survival fate of dendritic spines is central to understanding disease progression. Here we examined the visual response properties of layer 2/3 primary visual cortical dendrites and spines, using longitudinal two-photon calcium imaging in the P301S mouse model of tauopathy. Neuronal outputs in tau mutant mice were hyperactive and poorly tuned whereas dendritic spine responses were also poorly tuned but hypoactive. Moreover, in controls, stable spines were larger in size and more sharply tuned but less active compared to those that turned over. Such a function-to-structure relationship was absent in mutants. Our findings illustrate how tauopathy disrupts the preferential maintenance of well-tuned inputs in healthy neural circuitry, resulting in poorly tuned visual responses.

[This corrects the article DOI: 10.1039/D5SC06959A.].